Senior Software Engineer, HPC / Distributed Systems

Overview

We are seeking a Senior Software Engineer, HPC Scheduling Platform to help design, build, and scale a high-performance compute platform supporting large-scale research, machine learning, and batch workload execution.

This role sits on an HPC Scheduling team responsible for developing and operating distributed compute systems that enable complex research workloads to run efficiently across Kubernetes-based environments. The team is focused on pushing the boundaries of batch scheduling, multi-cluster orchestration, and scalable infrastructure for advanced ML and compute-intensive workloads.

A major focus of this role will be working on an open-source CNCF project used to support multi-cluster Kubernetes batch job scheduling at scale. This is a hands-on engineering position for someone who enjoys building production-grade software, working deeply with Kubernetes, and solving complex infrastructure challenges in high-scale environments.

The ideal candidate brings strong software engineering experience, a deep interest in Kubernetes and batch computing, and the ability to operate across infrastructure, distributed systems, and platform engineering.

Key Responsibilities

Software Engineering & Platform Development

• Design, develop, and maintain high-quality software solutions with a strong focus on Go/Golang
• Build scalable, reliable, and globally distributed systems that support large-scale research and ML workloads
• Contribute to the development and enhancement of Kubernetes-based scheduling platforms, including Armada
• Develop and maintain Kubernetes components such as controllers, operators, and custom platform services
• Apply strong software architecture, computer science fundamentals, and data structure knowledge to guide technical design and code quality

Kubernetes, Scheduling & Distributed Systems

• Build and operate containerized applications within Kubernetes environments
• Support advanced workload orchestration, scheduling, and batch processing across multi-cluster environments
• Work with HPC, Kubernetes, DAG-based workflows, and job scheduling systems such as Slurm
• Help improve scheduling efficiency, workload placement, resource utilization, and platform reliability
• Partner with engineering and research teams to support complex compute and ML workload requirements

Infrastructure, Data & Operations

• Manage and optimize data interactions across relational and non-relational systems, particularly PostgreSQL
• Support Linux-based systems as part of the core compute and scheduling platform
• Apply networking fundamentals to troubleshoot, optimize, and improve platform connectivity and performance
• Diagnose and resolve complex issues across software, infrastructure, Kubernetes, and distributed systems layers
• Operate systems at scale in cloud environments, ideally AWS

Observability, Automation & Best Practices

• Build and improve CI/CD pipelines, release processes, and platform engineering workflows
• Implement and support observability practices using tools such as Prometheus, Grafana, and logging platforms
• Work with event-driven systems and message queues such as Apache Kafka or Pulsar
• Drive continuous improvement across reliability, scalability, automation, and engineering standards
• Stay current with emerging technologies in Kubernetes, HPC, scheduling, and distributed systems

Required Qualifications

• Strong software engineering background with hands-on experience developing production systems in Go/Golang
• Experience developing Kubernetes components such as controllers, operators, or custom resources
• Experience building, operating, or supporting distributed systems at scale
• Strong working knowledge of Kubernetes, containers, Linux, and cloud infrastructure
• Experience with batch computing, workload scheduling, HPC, or DAG-based workflow systems
• Experience with PostgreSQL or similar relational database technologies
• Familiarity with message queues or event-driven platforms such as Kafka, Pulsar, or similar tools
• Experience with observability tools such as Prometheus, Grafana, logging systems, and operational dashboards
• Ability to independently troubleshoot complex technical issues across infrastructure and application layers
• Strong understanding of software design principles, data structures, and computer science fundamentals

Preferred Qualifications

• Experience with Armada, Slurm, Volcano, Kueue, or similar scheduling technologies
• Experience supporting ML, AI, research, or high-throughput compute workloads
• Experience operating large-scale Kubernetes environments across multiple clusters
• Experience with AWS or another major cloud provider
• Background contributing to open-source infrastructure, platform, or CNCF projects
• Experience with performance tuning, reliability engineering, and large-scale systems optimization

Ideal Profile

The ideal candidate is a hands-on software engineer with deep Kubernetes experience and a strong interest in batch scheduling, HPC, and distributed systems. This person should be comfortable writing production-grade Go, operating Linux and Kubernetes environments at scale, and solving complex scheduling and infrastructure challenges for high-performance research and ML workloads.